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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2641-2648

Personalized federated multi-task learning optimization method for heterogeneous data

Li Kea
Wang Xiaofenga,b
Wang Hua
a. School of Computer Science & Engineering, b. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0006

Abstract

Federated learning, a novel distributed machine learning paradigm, collaboratively trains a global model while preserving data privacy. It faces challenges of slow convergence and low accuracy in the global model under data heterogeneity. Aiming at the problem, this paper proposed a personalized federated multi-task learning optimization(FedMTO) algorithm tailored for heterogeneous data. In a multi-task learning framework that included global and local tasks, it considered the personalized federated optimization problem. Initially, FedMTO adopted the idea of parameter decomposition, coordinating global and local classifiers through the learning of adaptive classifier combination weights. This process extracted knowledge from global classifiers to achieve personalized modeling for local tasks. Furthermore, due to the varying data distributions of local tasks, FedMTO incorporated a regularization multi-task learning strategy during local updates. This approach focused on the relevance between tasks to reduce the differences among various local tasks, thus ensuring fairness in the federated learning process. Finally, experiments were conducted on the MNIST and CIFAR-10 datasets under different data heterogeneity scenarios. The results demonstrate that compared with existing algorithms, FedMTO achieves higher accuracy and better fairness, verifying the effectiveness of this method in addressing heterogeneous data problems in federated learning.

Foundation Support

国家自然科学基金资助项目(62062001)
宁夏青年拔尖人才项目(2021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0006
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Algorithm Research & Explore
Pages: 2641-2648
Serial Number: 1001-3695(2024)09-011-2641-08

Publish History

[2024-03-19] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

李可, 王晓峰, 王虎. 面向异构数据的个性化联邦多任务学习优化方法 [J]. 计算机应用研究, 2024, 41 (9): 2641-2648. (Li Ke, Wang Xiaofeng, Wang Hu. Personalized federated multi-task learning optimization method for heterogeneous data [J]. Application Research of Computers, 2024, 41 (9): 2641-2648. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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